0 Description

0.1 Data Description Three types of data were recorded in London between November 2011 and February 2014

0.2 Weather Data Description Features recorded in 1 hour resolution:

0.3 Introducing Temporal Features

Extracted temporal features:

Encoding features on unit circle:

1. Load Consumption Data

1.1 Selecting a Building: for 1 household, in half an hour resolution. We look for a building that has:

1.2 Cleaning Data

1.3 Add Temporal Factors

1.4 Add Weather Features

2 Visualizing Consumption Data

2.1 Plot monthly patterns

2.2 Box Plots

2.3FFT

2.3 Consumption vs. Time and Temperature

2.4 Consumption Heatmap vs. Time and Temperature Heating in the UK:

4.Autocorrelation plots:

4.1 ACF: Autocorrelation with confidence bounds

4.2 PACF: The partial autocorrelation function (PACF) plot shows the amount of autocorrelation at lag k that is not explained by lower-order autocorrelations – The partial autocorrelation at lag k is the coefficient of LAG(Y,k) in an AR(k) model, i.e., in a regression of Y on LAG(Y, 1), LAG(Y,2), ... up to LAG(Y,k)

Observations:

Plot suggests that the following time steps are relevant:

Recurrence Plots

Dickey Fuller Test